Mechanistic Insights into a Self-Management Intervention in Young Adults with Irritable Bowel Syndrome: A Pilot Multi-Omics Study

Abstract

Background: Self-directed lifestyle modifications are essential for managing symptoms in individuals diagnosed with irritable bowel syndrome (IBS). This study incorporated longitudinal multi-omics profiling to estimate the mechanisms underlying responses to a nurse-led person-centered self-management intervention in young adults with IBS. Methods: This pre-post study was nested within a 12-week parent randomized controlled trial (NCT03332537). Biospecimens (stool and blood) and clinical outcomes were collected at baseline and post-intervention. Symptoms were assessed using the Brief Pain Inventory and PROMIS® short forms. Host transcriptomic profiling was performed using RNA sequencing, and gut microbial composition was analyzed via 16S rRNA sequencing. Host transcriptomic co-expression and microbial co-abundance modules were identified via weighted gene co-expression network analysis. Associations between multi-omics modules and symptoms were evaluated using linear mixed-effect models. Results: Among the 20 participants, most were non-Hispanic (75%), White (75%), and female (65%). The intervention significantly reduced self-reported pain severity (p = 0.019) and pain interference (p = 0.013). Decreased associations were observed between pain phenotypes and a microbial module enriched in core metabolic pathways (interference: β = −4.7, p < 0.001; severity: β = −2.4, p = 0.02). Anxiety strengthened associations with host transcriptomic cellular energy metabolism pathways post-intervention (p < 0.05). The intervention attenuated associations between fatigue, sleep disturbance, and immune–inflammatory transcriptomic and microbial adaptation modules (p < 0.05). Conclusions: Findings suggest that the IBS self-management intervention induces symptom-specific biological responses, implicating distinct host–microbe pathways. Larger longitudinal studies are warranted to validate these omics-based symptom signatures.